Data analytics systems are becoming an increasingly important part of a growing number of industries as the amount of raw data produced or otherwise retained in those industries increases. Many companies utilize analytics in an effort to obtain meaningful insights into the nature and consequences of raw data. Data analytics systems assist in determining these insights by processing the raw data and automatically providing output indicating information that may be readily interpreted by end users. The overwhelming amount of data to be analyzed in such industries often results in challenges in deriving meaningful analytics and, therefore, insights.
A particular set of techniques and tools utilized for data analytics may be employed by business intelligence (BI) systems. Such BI systems acquire and transform raw data (e.g., structured, unstructured, semi-structured data, or a combination thereof) into information that is meaningful and useful for analyzing a business. Insights generated by BI systems may be utilized for decision-making purposes related to, e.g., operations (e.g., product positioning or pricing), strategy (e.g., priorities or goals), and the like. Such decision-making use may be further enhanced by incorporating information from external systems (e.g., external systems providing data related to the industry) with internal data of the company.
Existing solutions for providing BI data allow for querying of BI systems. Such querying utilizes computing resources. The amount of computing resources required for providing the queried information increases as the amount of data accessible to a BI system increases. This is particularly true for larger enterprises, which typically collect and utilize significantly more data in BI systems. As a result, processing of queries may take longer as a business collects and retains more information.
It would therefore be advantageous to provide a solution that would overcome the challenges noted above.